Adaptive Control Strategy for Oscillating Water Column Wave Energy Device Based on Deep Reinforcement Learning

Abstract

This paper proposes an adaptive control strategy for oscillating water column (OWC) wave energy devices based on deep reinforcement learning (DRL). By combining the feature extraction capability of deep neural networks with the decision optimization ability of reinforcement learning, a system is designed to dynamically adjust the turbine control strategy under varying wave conditions. Experimental results show that the proposed method effectively improves the energy conversion efficiency of the device, enhances its stability under extreme sea conditions, and significantly optimizes the system's long-term operational performance. The study demonstrates that deep reinforcement learning provides a feasible solution for intelligent control of wave energy devices.